1. Field of the Invention
The specification hereby incorporates by reference in their entirety under 37 C.F.R. § 1.77(b)(4), the files contained on the three compact discs filed herewith. The first and second compact discs each includes a file entitled “mmi1 100-1 Table 1A.txt,” created Sep. 23, 2004, which is 8,960 kilobytes in size, and a file entitled “mmi1 100-1 Table 1B.txt,” created Sep. 23, 2004, which is 11,817 kilobytes in size. The first and second compact discs are duplicates as required under 37 C.F.R. § 1.52(e)(4). The third compact disc includes a file entitled “MMI1 100-1. txt,” created Sep. 22, 2004, which is 88,025 kilobytes in size.
The invention relates generally to gene association analyses and more specifically to polymorphisms and associated traits of bovine species.
2. Background Information
Under the current standards established by the United States Department of Agriculture (USDA), beef from bulls, steers, and heifers is classified into eight different quality grades. Beginning with the highest and continuing to the lowest, the eight quality grades are prime, choice, select, standard, commercial, utility, cutter and canner. The characteristics which are used to classify beef include age, color, texture, firmness, and marbling, a term which is used to describe the relative amount of intramuscular fat of the beef. Well-marbled beef from bulls, steers, and heifers, i.e., beef that contains substantial amounts of intramuscular fat relative to muscle, tends to be classified as prime or choice; whereas, beef that is not marbled tends to be classified as select. Beef of a higher quality grade is typically sold at higher prices than a lower grade beef. For example, beef that is classified as “prime” or “choice,” typically, is sold at higher prices than beef that is classified into the lower quality grades.
Classification of beef into different quality grades occurs at the packing facility and involves visual inspection of the ribeye on a beef carcass that has been cut between the 12th and 13th rib prior to grading. However, the visual appraisal of a beef carcass cannot occur until the animal is harvested. Ultrasound can be used to give an indication of marbling prior to slaughter, but accuracy is low if ultrasound is done at a time significantly prior to harvest.
Currently there are no cost effective methods for identifying live cattle that give accurate prediction of the genetic potential to produce beef that is well-marbled. Such information could be used by feedlot operators to identify animals for purchase prior to finishing, to identify animals under contract for one or more premium programs administered by a packer, by feedlot managers to make management decisions regarding individual animals within a lot (including nutrition programs and sale dates), by cow-calf producers in marketing their animals to various feedlots or in making decisions regarding which animals will be sold on various carcass evaluation grids. Such information could also be used to identify cattle that are good candidates for breeding. Thus, it is desirable to have a method which can be used to assess the beef marbling potential of live cattle, particularly young cattle well in advance of the arrival of the animal at the packing house.
Another characteristic of beef that is desired by consumers is tenderness of the cooked product. Currently there are no procedures for identifying live animals whose beef, if cooked properly, would be tender. Currently, there are two types of procedures which are used by researchers to assess the tenderness of meat samples after they have been aged and subsequently cooked. The first involves a subjective analysis by a panel of trained testers. The second type is characterized by methods used to cut or shear meat samples that have been removed from an animal and aged. One such method is the Wamer-Bratzler shear force procedure which involves an instrumental measurement of the force required to shear core samples of whole muscle after cooking. Neither of these procedures can be used to any practical effect in a fabrication setting as the need to age product prior to testing would lead to maintenance of inventory of fabricated product that would be cost prohibitive. Consequently, the methods are used at research facilities but not at packing plants. Accordingly, it is desirable to have new methods which can be used to identify carcasses and live cattle that have the potential to provide beef that, if cooked properly, will be tender.
It has been difficult for the livestock industry to combine genetics for red meat yield and marbling and/or tenderness. In fact, conventional measurement techniques indicate that marbling and red meat yield tend to be antagonistic. Hence, there is a need for tools that identify superior genetic potential for the combination of red meat yield, tenderness and marbling. Another trait of interest is live cattle growth rate (average daily gain). Currently, cattle producers do not have tools to identify animals with superior genetic potential for rapid growth prior to purchase. In addition, there are no methods currently available to identify animals which combine capability for superior growth rate with desirable carcass characteristics.
While many methods of measurement and selection of cattle in feedlots have been tried, both visual and automated, such as ultrasound, none have been successful in accomplishing the desired end result. That end result is the ability to identify and select cattle with superior genetic potential for desirable characteristics and then manage a given animal with known genetic potential for shipment at the optimum time, considering the animal's condition, performance and market factors, the ability to grow the animal to its optimum individual potential of physical and economic performance, and the ability to record and preserve each animal's performance history in the feedlot and carcass data from the packing plant for use in cultivating and managing current and future animals for meat production. The beef industry is extremely concerned with its decreasing market share relative to pork and poultry. Yet to date, it has been unable to devise a system or method to accomplish on a large scale what is needed to manage the current diversity of cattle (i.e. least about 100 different breeds and co-mingled breeds) to improve the beef product quality and uniformity fast enough to remain competitive in the race for the consumer dollar spent on meat.
Modern day breeding programs in animal agriculture originated from fundamental observations made upon the first domestication of animals. Early humans observed differences in a broad range of characteristics between the offspring produced by mating different parents and they took advantage of this observation by only mating individuals that demonstrated the most desirable characteristics. By following this strategy for several generations our ancestors were able to create populations of animals that exhibited only desirable traits that best fit their needs. This strategy, called selective mating or selective breeding, is based on identifying the best progeny from one generation and making them the parents for the next generation. Selective breeding results in the development of individuals that are superior for one or more traits and is the backbone for modern day genetic improvement programs in animal agriculture.
Through the utilization of selective breeding strategies geneticists have been able to define the fundamental genetic parameters that influence the expression of traits. Breeding experiments revealed that some traits, like coat color, were expressed in a qualitative manner and could be easily passed onto the next generation while other traits, like growth rate or adult size, were expressed in a quantitative fashion and only small progress could be made at each generation. Subsequent research in the field of molecular genetics has now revealed that qualitative trait effects are caused by the action of a single gene while quantitative traits are caused by the action and interaction of many different genes.
In addition to contributions of genetics, it has been determined that genetic source alone did not account for all of the differences observed among groups of closely related individuals and that environment and management also played a role in determining the expression of specific traits. In order to account for all of the differences observed between individuals for a specific trait geneticists developed the equation; P (phenotype or overall trait expression)=G (genetic contribution from parents)+E (contribution from the environment). Geneticists observed that some traits respond better to selection than others due to intrinsic differences in G and E and developed scientific methods for determining the genetic contribution, or heritability, for a number of unique traits. For any given trait a higher heritability indicates more of the total variation is accounted for by the genetic source and a faster response to selection can be achieved. The parameters that govern differences in the expression of specific traits between individuals as defined above have been used for decades to make genetic improvement in animal agriculture production. Utilization of these parameters in a “Classical Breeding Program” provides breeders with a set of tools to evaluate the genetic makeup of different individuals within a population and to make steady progress in improving the expression of traits that have economic significance to the commercial production of livestock species.
The primary objective of any genetic improvement program is to ascertain the genetic potential of individuals for a broad range of economically important traits at a very early age. While the classical breeding approach has produced steady genetic improvement in livestock species it is limited by the fact that accurate prediction of an individual's genetic potential can only be achieved when the animal reaches adulthood (fertility and production traits) or is harvested (meat quality traits). This is particularly problematic for meat animals since harvested animals obviously cannot enter the breeding pool. Furthermore, it is difficult to utilize the classical breeding approach for traits that are difficult (disease resistance) or costly (meat tenderness) to measure.
To overcome the previous problems with the classical breeding approach animal breeders and geneticists turned to the new fields of molecular genetics and genomics. These disciplines offered the promise that the underlying genes responsible for genetic variation of important traits could be identified. Targeted research programs were initiated to ascertain the location and functional differences of specific genes that contribute to genetic variation for defined traits. The primary goal of molecular breeding programs in livestock species is to develop genetic assays for economically important traits that can be tested on individual animals at an early age, can be used for traits that are difficult to measure, that provide an accurate estimate of an animals genetic potential for expression of the trait, and account for a large proportion of the total genetic variation observed for the trait in commercial populations.
To date, three different experimental approaches have been utilized to identify genes that effect economically important traits in livestock species: Candidate Gene Approach, Differential Gene Expression Approach, and Within Family Quantitative Trait Loci (QTL) Linkage Approach. Limited success has been achieved for each of these methods in identifying genes that contribute to genetic variation for defined traits. However, each method also has limitations, as the primary objectives of the molecular breeding approach described above have not been achieved. Accordingly, a need exists for methods that assist in a determination of the genetic potential of individuals for a broad range of economically important traits at a very early age. A description of each of the experimental approaches attempted thus far, and the limitations for each is outlined below:
In the candidate gene approach a specific gene or set of genes is targeted based on the hypothesis they may have an effect on a particular trait. The hypothesis is developed based on existing information of biochemical pathways and the function of the gene in another species, most often human or mouse where substantial gene characterization has been performed. The known sequence of the human or mouse gene is used to fish-out the gene in the target species. The DNA sequence of the gene in the target species is determined by sequencing a large number of individuals and any sequence variation is cataloged. The sequence variations are developed into diagnostic assays and genotyped against a population of animals where phenotypic variation for the targeted traits has been characterized. The data set is analyzed to determine if statistically significant associations exist between specific sequence variants and expression of the trait.
The candidate gene approach has been successful in identifying genes and sequence variants that have an effect on a particular trait. However, this approach does have limitations and is analogous to finding a needle-in-the-haystack. With over 30,000 genes characterized in humans and mouse as a result of the whole genome sequence the first difficulty is identifying a gene that will actually contribute to genetic variation for a specific trait. Secondly, a large enough set of individuals must be sequenced to find the sequence variant that is responsible for or at least highly associated with the effect. And finally, if an effect is present at all the population of animals screened must be large enough to ensure statistically significant association of the effect. While it is feasible to meet all of these conditions to discover significant associations the cost of this approach is high because it is a random method that cannot be targeted to genes that have the largest effect.
In the differential gene expression approach, differences in gene expression are characterized for specific genes and in targeted tissues with the hope of identifying genes that may be contributing to the observed genetic variation for a particular trait. As in the Candidate Gene Approach, targeted genes and tissues are chosen based on existing information of biochemical pathways and the functions of genes in other species. Differential gene expression has been effective in identifying genes that are turned on or off by extreme differences in environment or by disease, but has been less successful in identifying genes that contribute to phenotypic variation in livestock production traits. Current technology platforms for detecting differences in gene expression require large differences in gene expression, often up to a 2 to 3 fold increase or decrease. Gene expression differences that may account for genetic variation in livestock traits may be under the detection threshold for existing gene expression technology.
Differential gene expression technology has been successfully used to elucidate biochemical pathways and to understand basic cellular functions but has not demonstrated any utility in the development of diagnostic assays to predict genetic potential of animals for specific traits. Even if differential expression of a gene is observed and can be directly attributed to phenotypic variation for a trait there is no guarantee that a sequence variant can be found in the gene or that the sequence variant is responsible for the effect. In many situations sequence variants for differentially expressed genes do not association with the observed difference in phenotypes. This could be explained by the action of other genes or gene products that regulate the expression of the differentially expressed gene but are located elsewhere in the genome.
In the within-family QTL linkage approach, small families of related individuals are bred-up or assembled, DNA samples are taken from all individuals in the population, phenotypic measurements for the targeted traits are taken on the progeny and a set of polymorphic DNA markers that span the genome are genotyped against the entire research population. The data set is then analyzed to determine if a particular marker or a linked set of markers have specific allele(s) that predominately associate with the phenotypic variation observed in the progeny from a specific parent or set of parents. A large number of research reports claiming linkage between specific traits and markers have been published for a wide variety of traits and in several different livestock species.
Although the within family QTL linkage approach has resulted in a number of reported linkages between targeted traits and specific marker locations this approach does not result in the direct development of diagnostic assays that can predict an animals genetic potential for the targeted trait. In practice, the research populations used for these experiments are very small, often only representing two or three different sire families, and as such, they do not represent the broad pattern of genetic variation that is observed across commercial animal populations. These small research populations are also problematic because the QTL can only be identified when it is heterozygous for a particular family group. Linkages between a marker and a trait are determined by allele frequency differences in the marker between progeny separated into groups with high versus low expression for the trait. This implies that the QTL itself must be heterozygous in order to be detected and the smaller the population the less likely it is to find QTLs in a heterozygous state. Furthermore, research populations designed to identify linkages in livestock species are usually half-sib designs where it is only possible to measure the genetic variation contributed by the male side of the pedigree. Half-sib designs have limited effectiveness in discovering significant linkages because only one-half of the genetic variation is accounted for in the analysis. Finally, the research populations are often comprised of animals and/or breed types that have extreme phenotypic differences for the targeted traits to insure the discovery of markers that demonstrate linkage to the trait. These extreme phenotypic crosses do not represent mainstream industry breeding practices and therefore, any reported linkage is suspect because it may only exist as an artifact within the research population and may not actually be segregating in commercial animal breeding populations.
Another limitation of the within family QTL approach is the lack of marker density for the linkage map used in the study. Due to cost and genotyping throughput issues all reported QTL linkage studies performed to date in livestock species have only used 100 to 200 total markers to cover the entire genome. With such a limited number of markers it is impossible to pinpoint the exact location of the QTL on the chromosome. Linkage distances ranging from 3 to over 60 centi-Morgans are commonly reported between the QTL and the linked marker(s). These broad linkage groups can actually span an entire chromosome and contain thousands of genes that are possible candidates for the observed effect. Because of these large distances, recombination between homologous chromosomes does not allow the use of linked markers identified in research populations to be used as predictors of genetic potential in commercial animal populations. Markers linked to QTLs can provide clues about the potential location of genes that have effects for certain traits but substantial additional research and validation is required to accurately pinpoint the location of the gene responsible for the effect and develop diagnostic assays to predict the expression of the trait.
In summary, three different experimental approaches have been used with limited success to identify genes, chromosomal regions or DNA markers that account for a large proportion of the genetic variation observed in economically important traits in livestock species. The results achieved from research programs utilizing these methods have not been widely utilized to date because they do not account for enough of the total genetic variation to allow accurate prediction of an animal's performance for a specific trait. Furthermore, even when successful these approaches are only capable of identifying additive genetic components while ignoring non-additive genetic components such as dominance (i.e. dominating trait of an allele of one gene over an allele of a another gene) and epistasis (i.e. interaction between genes at different loci) which are critical to the development of diagnostics that can be utilized by animal breeders to accurately predict genetic potential for economically important traits in livestock species.